Visual map analysis method and system for waste paper recycling station layout

Abstract

The embodiment of the application provides a kind of waste paper recycling station layout visual map analysis method and system, first, the historical operation data set of multiple historical recycling stations in target area is acquired, cover distribution location, service coverage and recycling quantity fluctuation information, then the feature extraction is carried out to the historical operation data set, generate spatial distribution feature set and resource matching feature set, then based on the preset layout optimization model, spatial distribution feature set and resource matching feature set are dynamically matched and analyzed and handled, generate recycling station layout optimization scheme, according to the optimization position coordinate and optimization service radius in recycling station layout optimization scheme, generate visual map mark data, finally, the data is superimposed to target area electronic map by calling map rendering engine, generate visual map interface containing recycling station layout optimization identification, can realize the scientific optimization and intuitive display of waste paper recycling station layout.

Description

Technical Field

[0001] This invention relates to the field of digital analysis technology, and more specifically, to a method and system for visual map analysis of the layout of waste paper recycling stations. Background Technology

[0002] With increasing environmental awareness and growing resource scarcity, the importance of the waste paper recycling industry is becoming increasingly prominent. As a key node in the waste paper recycling system, the rationality of the layout of waste paper recycling stations directly affects the efficiency, cost, and effective utilization of resources in waste paper recycling.

[0003] Currently, the layout of waste paper recycling stations relies primarily on manual experience or simple statistical analysis methods. Manual experience-based planning often lacks scientific basis and fails to comprehensively consider factors such as population distribution, traffic conditions, and waste paper generation within the target area, leading to irrational station layouts and situations where stations are either too densely or too sparsely distributed in some areas. While simple statistical analysis methods can provide some data support, they typically only analyze single factors and cannot comprehensively consider the interrelationships between various factors, making it difficult to formulate an optimal station layout plan. This results in problems such as low recycling efficiency, high transportation costs, and resource waste in the waste paper recycling process, failing to meet the demands of the modern waste paper recycling industry for efficient and scientific layout. Therefore, a method is needed that can comprehensively consider multiple factors to scientifically analyze and optimize the layout of waste paper recycling stations. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method and system for visual map analysis of the layout of waste paper recycling stations.

[0005] According to one aspect of the present invention, a method for visual map analysis of the layout of waste paper recycling stations is provided, the method comprising:

[0006] Obtain a set of historical operation data for multiple historical recycling stations within the target area. The set of historical operation data includes the distribution location information, service coverage information, and recycling volume fluctuation information of each recycling station.

[0007] Feature extraction is performed on the historical operational data set to generate a spatial distribution feature set and a resource matching feature set for each recycling station;

[0008] Based on a preset layout optimization model, dynamic matching analysis is performed on the spatial distribution feature set and the resource matching feature set to generate a recycling station layout optimization scheme for the target area.

[0009] Based on the optimized location coordinates and optimized service radius in the recycling station layout optimization scheme, generate visual map marker data;

[0010] The map rendering engine is invoked to overlay the visualized map marker data onto the electronic map of the target area, generating a visualized map interface that includes the recycle bin layout optimization marker.

[0011] According to another aspect of the present invention, a visualization map analysis system for the layout of waste paper recycling stations is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store a computer program; and the processor is used to execute the computer program to implement the visualization map analysis method steps for the layout of waste paper recycling stations as described above.

[0012] According to another aspect of the present invention, a readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, can perform the steps of the above-described method for visual map analysis of the layout of waste paper recycling stations.

[0013] By acquiring historical operational data sets from multiple recycling stations within the target area through any of the above methods, comprehensive information such as the distribution location, service coverage, and fluctuations in recycling volume of the recycling stations is obtained. Feature extraction is performed on the historical operational data sets to generate spatial distribution feature sets and resource matching feature sets. This allows for in-depth exploration of the intrinsic relationship between recycling station layout and resource utilization. Based on a preset layout optimization model, dynamic matching analysis is performed on the spatial distribution feature sets and resource matching feature sets. The generated recycling station layout optimization scheme fully considers the comprehensive influence of multiple factors. Visual map marker data is generated based on the recycling station layout optimization scheme, and the map rendering engine is used to overlay it onto the electronic map of the target area, generating a visual map interface containing recycling station layout optimization markers. This intuitively displays the optimized recycling station layout, providing decision-makers with a clear and convenient reference, which can significantly improve the rationality of waste paper recycling station layout, reduce recycling costs, improve recycling efficiency, and promote the effective utilization of resources.

[0014] To make the above-mentioned objects, features and advantages of the embodiments of the present invention more apparent and understandable, a detailed description will be given below in conjunction with the embodiments and the accompanying drawings. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This invention provides a schematic diagram of the components of a visualization map analysis system for the layout of waste paper recycling stations.

[0017] Figure 2 A flowchart illustrating the visualization map analysis method for the layout of waste paper recycling stations provided in an embodiment of the present invention is shown. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The terms “first,” “second,” “third,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] Figure 1A schematic diagram of exemplary components of a visual map analysis system 100 for waste paper recycling station layout is shown. The visual map analysis system 100 for waste paper recycling station layout may include one or more processors 104, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. The visual map analysis system 100 for waste paper recycling station layout may also include any storage medium 106 for storing any kind of information such as code, settings, data, etc. Non-limitingly, for example, storage medium 106 may include any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any storage medium can use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of the visual map analysis system 100 for waste paper recycling station layout. In one case, when processor 104 executes dependent instructions stored in any storage medium or combination of storage media, the visual map analysis system 100 for waste paper recycling station layout can perform any operation of the associated instructions. The visualization map analysis system 100 for the layout of waste paper recycling stations also includes one or more drive units 108 for interacting with any storage medium, such as hard disk drive units, optical disk drive units, etc.

[0021] The visual map analysis system 100 for waste paper recycling station layout also includes input / output 110 (I / O) for receiving various inputs (via input unit 112) and providing various outputs (via output unit 114). A specific output mechanism may include a presentation device 116 and a dependent graphical user interface (GUI) 118. The visual map analysis system 100 for waste paper recycling station layout may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the components described above together.

[0022] The communication unit 122 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, a visual map analysis system 100 for naming waste paper recycling station layouts, etc., governed by any protocol or combination of protocols.

[0023] Figure 2 This diagram illustrates a flowchart of a visualization map analysis method and system for the layout of waste paper recycling stations provided in an embodiment of the present invention. The visualization map analysis method and system for the layout of waste paper recycling stations can be derived from... Figure 1The visualization map analysis system 100 for the layout of the waste paper recycling station shown in the figure is executed. The detailed steps of the visualization map analysis method for the layout of the waste paper recycling station are described below.

[0024] Step S110: Obtain a set of historical operation data for multiple historical recycling stations within the target area. The set of historical operation data includes the distribution location information, service coverage information, and recycling volume fluctuation information of each recycling station.

[0025] In this embodiment, to rationally plan the layout of waste paper recycling stations and generate visualized map analysis results, it is first necessary to collect historical operational data sets from multiple historical recycling stations within the target area. The target area can be a specific region of a city or a specific geographical area. There are various ways to obtain this data, such as collaborating with the management departments of the recycling stations to extract relevant data from their databases; or utilizing a professional data acquisition platform to obtain data through a network interface.

[0026] Location information can be obtained using Geographic Information System (GIS) technology. Each recycling station has a specific location in geographic space, usually represented precisely by latitude and longitude coordinates. Let the location of a recycling station be represented by coordinates (X, Y), where X represents longitude and Y represents latitude. For multiple recycling stations, there will be a series of coordinate pairs (X1, Y1), (X2, Y2), ..., (Xn, Yn), where n represents the number of recycling stations.

[0027] Service coverage information can be obtained by analyzing the recycle bin's business records. Recycle bins typically record the addresses of their customers; spatial analysis of these addresses can determine the service coverage area. The service coverage area can be viewed as a specific region centered on the recycle bin, which can be represented by a polygon. For example, by connecting the furthest customer addresses within the service area to form a closed polygon, the area enclosed by this polygon is the recycle bin's service coverage area.

[0028] Obtaining information on fluctuations in recycling volume requires organizing and analyzing the daily recycling records of the recycling station. Recycling volume data is typically recorded at certain time periods, such as daily, weekly, or monthly. Let the time period be T. Under different time periods t1, t2, ..., tm, the recycling volume of the recycling station is Rt1, Rt2, ..., Rtm, respectively. The changes in these recycling volume data can reflect the fluctuation characteristics of recycling volume over time.

[0029] Step S120: Extract features from the historical operation data set to generate a spatial distribution feature set and a resource matching feature set for each recycling station.

[0030] After obtaining the historical operational data set, the next step is to extract features from this data to facilitate subsequent layout optimization analysis. The purpose of feature extraction is to extract key information from the raw data that reflects the spatial distribution and resource matching of recycling stations.

[0031] Step S121: Calculate the spacing features between each recycling station and its adjacent recycling stations based on the distribution location information, and construct coverage overlap features based on the service coverage information.

[0032] In this step, the spacing characteristics are calculated based on the distribution location information. To calculate the spacing between two recycling stations, a suitable distance metric is needed. Commonly used distance metrics in geospatial environments include Euclidean distance and Manhattan distance. Taking Euclidean distance as an example, let the coordinates of two recycling stations be (Xi, Yi) and (Xj, Yj), respectively. The Euclidean distance Dij between them can be calculated as follows: First, calculate the difference between the two coordinates on the X and Y axes, i.e., ΔX = Xi - Xj, ΔY = Yi - Yj. Then, according to the Pythagorean theorem, Dij is equal to the square root of the sum of the squares of ΔX and ΔY. For each recycling station, the spacing between it and its adjacent recycling stations can be calculated, resulting in a series of spacing values ​​Dij, which constitute the spacing characteristics.

[0033] The construction of overlap features requires information about service coverage. The service coverage of each recycle bin is represented by a polygon. Spatial analysis techniques are used to calculate the overlap between the polygons representing the service coverage of different recycle bins. The area of ​​the overlapping portion of two polygons can be calculated. Let the service coverage polygons of two recycle bins be P1 and P2, and the area of ​​their overlapping portion be S_overlap. This overlapping area is compared with the area of ​​each polygon individually, for example, by calculating the proportion of the overlapping area to the area of ​​each polygon, thus obtaining the overlap ratio value. For multiple recycle bins, there will be a series of overlap ratio values, which constitute the overlap features.

[0034] Step S122: Identify periodic change characteristics based on the recovery volume fluctuation information, and extract the recovery volume difference characteristics within adjacent time periods.

[0035] Periodic variations can be identified based on recovery volume fluctuations. Time series analysis of recovery volume data Rt1, Rt2, ..., Rtm can reveal potential periodic patterns. For example, Fourier transforms can be used to convert the time series data to the frequency domain, analyzing the amplitude and phase of different frequency components to determine the presence of significant periodicity. If analysis reveals a periodic variation with a period of T_period, then the recovery volume will exhibit similar patterns within each period.

[0036] To extract the characteristics of recovery differences within adjacent time periods, the recovery difference between adjacent time periods is calculated. Let two adjacent time periods be t and t+1, then the recovery difference ΔRt = Rt+1 - Rt. For all adjacent time periods, the corresponding recovery difference is calculated, resulting in a series of differences ΔRt. These differences constitute the recovery difference characteristics within adjacent time periods.

[0037] Step S123: Perform spatial clustering analysis on the spacing features and the coverage overlap features to generate density distribution features that reflect the degree of regional concentration.

[0038] After obtaining the spacing and overlap features, spatial clustering analysis is performed on them. The purpose of spatial clustering analysis is to group spatially adjacent recycling stations with similar features into the same cluster. Algorithms such as DBSCAN (a density-based spatial clustering application) can be used for clustering analysis.

[0039] First, a feature vector is constructed for each recycle bin based on the spacing feature and the coverage overlap feature. Let the spacing feature be D=[D1, D2, ..., Dk] and the coverage overlap feature be O=[O1, O2, ..., Ol], and combine them into a feature vector F=[D, O].

[0040] Then, the DBSCAN algorithm is used for clustering. This algorithm requires two parameters: the neighborhood radius ε and the minimum number of points MinPts. For each recycling station, if the number of points within its neighborhood of radius ε is greater than or equal to MinPts, that station is considered a core station. Core stations and their neighbors are grouped into the same cluster.

[0041] Cluster analysis is used to divide the recycling stations into different clusters. For each cluster, the density of recycling stations within it can be calculated, which is the ratio of the number of recycling stations in the cluster to the spatial area occupied by the cluster. These density values ​​constitute the density distribution characteristics that reflect the degree of concentration in the area.

[0042] Step S124: Perform time-series correlation analysis on the periodic change characteristics and the recovery volume difference characteristics to generate load fluctuation characteristics that reflect the degree of dynamic matching of resources.

[0043] A time-series correlation analysis was conducted between the periodic variation characteristics and the recovery volume difference characteristics. The purpose of the time-series correlation analysis is to find the intrinsic relationship between the periodic variation and the recovery volume difference, thereby generating load fluctuation characteristics that reflect the degree of dynamic matching of resources.

[0044] Time series correlation analysis methods, such as the Pearson correlation coefficient, can be used to measure the correlation between periodic variation characteristics and recovery variation characteristics. Let the periodic variation characteristics be represented by the time series P = [P1, P2, ..., Pm], and the recovery variation characteristics by the time series ΔR = [ΔR1, ΔR2, ..., ΔRm]. Calculate the Pearson correlation coefficient r between them. The closer r is to 1, the stronger the positive correlation between the two; the closer it is to -1, the stronger the negative correlation; and the closer r is to 0, the weaker the correlation.

[0045] In addition to correlation analysis, further analysis can be conducted on the changes in recovery volume differences across different cyclical phases. For example, during the rising, falling, and stable phases of cyclical changes, statistical measures such as the average and standard deviation of recovery volume differences can be calculated. These statistical measures, together with the correlation coefficient, constitute the load fluctuation characteristics that reflect the degree of dynamic resource matching.

[0046] Step S125: Map the density distribution features and the load fluctuation features to the spatial distribution feature set and the resource matching feature set, respectively.

[0047] The obtained density distribution characteristics and load fluctuation characteristics are mapped to a spatial distribution feature set and a resource matching feature set, respectively. The spatial distribution feature set is mainly used to describe the spatial distribution of recycling stations, while the resource matching feature set is used to describe the resource matching of recycling stations.

[0048] Density distribution features are included as part of the spatial distribution feature set. This is because density distribution features reflect the concentration of recycling stations within a region, which is closely related to the spatial distribution of recycling stations. For example, the density value of each cluster can be added as an element to the spatial distribution feature set.

[0049] Load fluctuation characteristics are included as part of the resource matching feature set. Load fluctuation characteristics reflect the dynamic changes in recycling volume and are related to the degree of resource matching at recycling stations. For example, correlation coefficients and statistical measures of recycling volume differences at different cycle stages can be added as elements to the resource matching feature set.

[0050] Step S130: Based on the preset layout optimization model, perform dynamic matching analysis on the spatial distribution feature set and the resource matching feature set to generate a recycling station layout optimization scheme for the target area.

[0051] After obtaining the spatial distribution feature set and resource matching feature set, a preset layout optimization model is used to perform dynamic matching analysis on them to generate a recycling station layout optimization scheme for the target area.

[0052] Step S131: Input the density distribution feature into the first analysis layer of the layout optimization model to generate a set of location candidates that meet the preset coverage conditions.

[0053] In this step, the density distribution feature is input into the first analysis layer of the layout optimization model. The main function of the first analysis layer of the layout optimization model is to screen out location candidate points that meet the preset coverage conditions based on the density distribution feature.

[0054] First, divide multiple priority coverage sub-regions according to the regional concentration degree in the density distribution feature. The regions can be divided into different levels according to the size of the density value, such as three levels of high, medium, and low priority coverage sub-regions. Let the density thresholds be D1 and D2 (D1 < D2) respectively. When the density value is greater than D2, this region is a high-priority coverage sub-region; when the density value is between D1 and D2, this region is a medium-priority coverage sub-region; when the density value is less than D1, this region is a low-priority coverage sub-region.

[0055] Identify vacant location points that do not meet the service radius requirement in the priority coverage sub-regions with a priority lower than the first set priority (such as low priority). According to the preset service radius requirement, find out the location points in these sub-regions that are not covered by the existing recycling stations.

[0056] Calculate the overload working coefficient of the existing recycling stations in the coverage sub-regions with a priority higher than the second set priority (such as high priority). The overload working coefficient can be calculated based on the historical recycling volume and the designed processing capacity of the recycling station. Let the historical recycling volume of the recycling station be R and the designed processing capacity be C, then the overload working coefficient = R / C.

[0057] Combine the vacant location points and the overload working coefficient to generate a set of location candidates including new candidate points and expansion candidate points. New candidate points refer to setting up new recycling stations at the vacant location points, and expansion candidate points refer to expanding the existing recycling stations with a higher overload working coefficient.

[0058] Finally, verify the connectivity characteristics of each candidate point in the set of location candidates with the traffic road network, and filter out the candidate points that do not meet the preset accessibility conditions.

[0059] Step S1311: Obtain the traffic network topology data of the target area and extract the vehicle traffic density characteristics of the roads where each candidate point is located.

[0060] To verify the accessibility of the candidate points, first obtain the traffic network topology data of the target area. These data can be obtained from the traffic management department or relevant geographic information databases. The traffic network topology data describes the connection relationship and layout of the roads in the target area.

[0061] For each candidate point, determine the road it is located on and extract the vehicle traffic density characteristics of that road. Vehicle traffic density can be obtained through traffic flow monitoring equipment or estimated using a traffic model. Let ρ be the vehicle traffic density of the road where a candidate point is located.

[0062] Step S1312: Calculate the access distance between each candidate point and the nearest main road, and generate an accessibility score by associating the vehicle traffic density feature.

[0063] Calculate the access distance between each candidate point and the nearest main road. The spatial analysis function of a Geographic Information System (GIS) can be used to find the shortest path from the candidate point to the nearest main road and calculate the length of that path, denoted as d.

[0064] Accessibility scores are generated by associating vehicle traffic density characteristics with access distance. The accessibility score can be obtained through a comprehensive calculation formula, such as accessibility score = f(ρ, d), where f is a predefined function that takes into account the impact of vehicle traffic density and access distance on accessibility.

[0065] Step S1313: Analyze the average transportation time characteristics of each candidate point based on the historical transportation vehicle trajectory data of the recycling station.

[0066] Based on historical vehicle trajectory data from recycling stations, the average transportation time characteristics of each candidate point were analyzed. The vehicle trajectory data records the travel path and time of the vehicles from the recycling station to the destination.

[0067] For each candidate point, the transportation time of the transport vehicles from that candidate point to each destination is statistically analyzed, and the average value is calculated to obtain the average transportation time characteristic, denoted as t.

[0068] Step S1314: The accessibility score and the average transportation time feature are weighted and calculated to generate a comprehensive transportation efficiency index.

[0069] The accessibility score and average transportation time feature are weighted and calculated to generate a comprehensive transportation efficiency index. Let the weight of the accessibility score be w1, and the weight of the average transportation time feature be w2 (w1+w2=1), then the comprehensive transportation efficiency index = w1*accessibility score + w2*t.

[0070] Step S1315: When the comprehensive transportation efficiency index fails to meet the preset standard, the corresponding candidate point is removed from the location candidate set.

[0071] The overall transportation efficiency index is compared with a preset standard. If the overall transportation efficiency index fails to meet the preset standard, it indicates that the accessibility and transportation efficiency of the candidate point are poor, and the candidate point is removed from the location candidate set.

[0072] Step S132: Input the load fluctuation characteristics into the second analysis layer of the layout optimization model to calculate the resource matching score for each candidate location.

[0073] The load fluctuation characteristics are input into the second analysis layer of the layout optimization model. The main function of the second analysis layer is to calculate the resource matching score for each candidate location based on the load fluctuation characteristics.

[0074] For each candidate location, a resource matching score is calculated by combining information such as the correlation coefficient in the load fluctuation characteristics, the statistical differences in recovery volume across different cycle stages, and the potential recovery demand in the area where the candidate location is located. A machine learning-based scoring model can be used, whose inputs are the load fluctuation characteristics and the potential recovery demand characteristics, and whose output is the resource matching score. Let S be the resource matching score for a candidate location.

[0075] Step S133: Sort the candidate location set according to the resource matching score, and select the optimized location coordinates that meet the score threshold.

[0076] The candidate location set is sorted based on resource matching scores. Candidate points can be arranged in descending order of score.

[0077] Then, the optimized location coordinates that meet the scoring threshold are selected. Let the scoring threshold be S_threshold, and the location coordinates of candidate points with resource matching scores greater than or equal to S_threshold are used as the optimized location coordinates.

[0078] Step S134: Based on the historical recovery data of the optimized location coordinates, dynamically adjust the boundary range of the optimized service radius.

[0079] Based on historical recycling volume data of the optimized location coordinates, the boundary range of the optimized service radius is dynamically adjusted. Historical recycling volume data can reflect the recycling demand in the vicinity of that location.

[0080] Based on the magnitude and distribution of historical recycling data, a reasonable service radius is determined. For example, if historical recycling volume is large and concentrated, the service radius can be appropriately expanded; if historical recycling volume is small and dispersed, the service radius can be appropriately reduced. By continuously adjusting the service radius, the recycling station at that location can better meet the recycling needs of the surrounding area.

[0081] Step S135: Generate a layout optimization scheme containing conflict detection results based on the positional relationship between the boundary range and adjacent recycling stations.

[0082] Step S1351: Calculate the difference between the boundary range of the optimized service radius and the preset distance threshold to generate the first conflict detection index.

[0083] Calculate the difference between the boundary range of the optimized service radius and the preset distance threshold. Let the optimized service radius be r, and the preset distance threshold be r_threshold, then the first conflict detection index = r - r_threshold.

[0084] Step S1352: Identify the geographical location data of residential or commercial areas within the boundary range and generate a second conflict detection index.

[0085] Identify and optimize the geographic location data of residential or commercial areas within the service radius boundary. This data can be obtained through a geographic information system (GIS).

[0086] A second conflict detection index is generated based on information such as the size and population density of residential or commercial areas. For example, a comprehensive index can be calculated based on the population size of a residential area and the intensity of commercial activity in a commercial area.

[0087] Step S1353: Predict the future load peak within the optimized service radius based on historical recovery data, and generate a third conflict detection index.

[0088] Predicting future peak loads within the optimized service radius based on historical recovery data. Time series forecasting models, such as the ARIMA model, can be used to analyze and predict historical recovery data.

[0089] The predicted future load peak is used as the third conflict detection indicator.

[0090] Step S1354: Input the first conflict detection index, the second conflict detection index and the third conflict detection index into the conflict decision model to generate a conflict level score.

[0091] The first, second, and third conflict detection metrics are input into the conflict decision-making model. The conflict decision-making model can be a rule-based model or a machine learning model.

[0092] The model generates a conflict level score based on three input conflict detection metrics. Let the conflict level score be C.

[0093] Step S1355: When the conflict level score exceeds the preset threshold, mark the coordinates of the optimized positions that need to be adjusted and the corresponding adjustment priority in the layout optimization scheme.

[0094] The conflict level score is compared with a preset threshold. If the conflict level score exceeds the preset threshold, it indicates that there is a significant risk of conflict at the optimization location.

[0095] Mark the coordinates of the locations to be adjusted in the layout optimization plan, and determine the corresponding adjustment priority based on the severity of the conflict. For example, the more severe the conflict, the higher the adjustment priority.

[0096] Step S140: Generate visual map marker data based on the optimized location coordinates and optimized service radius in the recycling station layout optimization scheme.

[0097] Based on the optimized location coordinates and optimized service radius in the recycling station layout optimization plan, visualized map marker data is generated. The optimized location coordinates are used to determine the specific location on the map, and the optimized service radius is used to determine the size of the service area.

[0098] For each optimized location coordinate, a circular area is drawn with that coordinate as the center and the optimized service radius as the radius. This circular area represents the service range of that recycler station. Simultaneously, corresponding labeling information, such as the recycler station's number and name, is added to each optimized location and service range. This labeling information and the related data of the circular area constitute the visualization map labeling data.

[0099] Step S150: Call the map rendering engine to overlay the visualized map marker data onto the electronic map of the target area to generate a visualized map interface containing the recycling bin layout optimization mark.

[0100] Step S151: Extract the geocoding information of the optimized location coordinates and associate it with the boundary coordinate data of the optimized service radius.

[0101] Extract and optimize geocoding information from location coordinates. Geocoding information converts geographic coordinates into easily identifiable address information, such as street names and house numbers. Geocoding information can be obtained using the API of a geocoding service provider.

[0102] The boundary coordinate data of the optimized service radius is correlated. The boundary of the optimized service radius can be represented by a series of coordinate points. These boundary coordinate data are correlated with the geocoding information of the optimized location so that the service area can be accurately displayed on the map later.

[0103] Step S152: Generate a first marker layer in the electronic map based on the geocoding information. The first marker layer includes a recycling bin location marker and a service radius ring marker.

[0104] Based on geocoding information, a first marker layer is generated in the electronic map. The geocoding information provides the specific location of the recycling station on the map; based on this location information, recycling station location markers are drawn on the electronic map. This recycling station location marker can be a specific icon used to visually represent the recycling station's location.

[0105] Simultaneously, a ring-shaped marker for the service radius is drawn based on the boundary coordinate data of the optimized service radius. This ring is centered on the location of the recycle bin, and its radius is determined by the optimized service radius. During the drawing process, the accuracy and precision of the ring marker must be ensured to truly reflect the service area of ​​the recycle bin. The drawing tools provided by the map rendering engine can be used to connect the points sequentially based on the boundary coordinate data, forming a closed ring.

[0106] Step S153: Identify overlapping areas with existing recycling bins in the boundary coordinate data and generate a second marking layer. The second marking layer contains conflict warning labels and optimization suggestion text.

[0107] Step S1531: Extract the polygon vertex sequence from the boundary coordinate data of the optimized service radius.

[0108] Extract the polygon vertex sequence from the boundary coordinate data of the optimized service radius. The boundary of the optimized service radius can typically be viewed as a polygon, and its boundary coordinate data consists of a series of coordinate points. These coordinate points are extracted in a certain order (such as clockwise or counterclockwise) to form the polygon vertex sequence. Each vertex in this polygon vertex sequence corresponds to a specific location on the boundary, and these vertices can accurately describe the boundary shape of the optimized service radius.

[0109] Step S1532: Calculate the ratio of the intersection area between the polygon vertex sequence and the polygons within the existing recycling bin service area.

[0110] The algorithm calculates the ratio of the intersection area between the polygon formed by the extracted sequence of polygon vertices and the polygon within the existing recycle bin service area. First, a spatial analysis algorithm is used to determine if two polygons intersect. If they intersect, the area of ​​the intersection is further calculated. The intersection region can be divided into multiple smaller triangles or quadrilaterals; the total area of ​​the intersection is obtained by calculating and summing the areas of these smaller shapes.

[0111] Then, the area of ​​the intersecting portion is compared with the area of ​​the optimized service radius polygon and the area of ​​the existing recycling station service area polygon, respectively, to calculate the proportion of the intersecting area to the area of ​​each polygon. Let the area of ​​the optimized service radius polygon be A1, the area of ​​the existing recycling station service area polygon be A2, and the area of ​​the intersecting portion be A_overlap. The proportion of the intersecting area can be expressed as two ratios: A_overlap / A1 and A_overlap / A2.

[0112] Step S1533: When the ratio of the intersecting areas exceeds the preset overlap threshold, a pulse warning sign is generated at the center point of the intersecting area.

[0113] The calculated intersection area ratio is compared with a preset overlap threshold. The preset overlap threshold is a standard value set based on actual conditions to determine whether the overlap between two service areas is excessive. If the intersection area ratio exceeds the preset overlap threshold, it indicates a significant overlap between the service areas of the two recycling stations, which may lead to resource waste or competition.

[0114] In this scenario, a pulsed warning sign is generated at the center point of the intersecting area. First, the center point is determined by calculating the geometric center of the intersecting area. This can be done using a polygon centroid calculation method, which involves taking a weighted average of the coordinates of all vertices within the intersecting area. Then, a pulsed warning sign is drawn at this center point. This sign can be a flashing icon or a dynamic graphic to attract the user's attention.

[0115] Step S1534: Obtain the historical conflict resolution records of the existing recycle bins and generate optimization suggestion text associated with the pulse warning icon.

[0116] Obtain historical conflict resolution records from existing recycle bins. These records can be obtained from the recycle bin's management system or relevant databases and contain experience and methods for handling issues such as service area conflicts in the past.

[0117] Based on historical conflict resolution records and the current conflict situation, optimization suggestion text associated with the pulse warning icon is generated. For example, if historical records show that adjusting service hours or service areas can effectively resolve conflicts, then corresponding suggestions can be made in the optimization suggestion text. The optimization suggestion text can include specific adjustment measures, implementation steps, and other information for user reference.

[0118] Step S1535: Dynamically adjust the flashing frequency and color depth of the pulse warning sign according to the size of the intersection area ratio.

[0119] Step S15351: Establish a mapping table between the ratio of intersecting areas and the warning sign parameters, wherein the warning sign parameters include frequency level and color code.

[0120] Establish a mapping table between the ratio of intersecting areas and the parameters of the warning signs. The main parameters of the warning signs include frequency level and color code. The frequency level is used to control the flashing frequency of the pulse warning signs, and the color code is used to determine the color of the warning signs.

[0121] The proportions of intersecting areas are divided into different intervals, and a corresponding frequency level and color code are assigned to each interval. For example, when the proportion of intersecting areas is in a lower interval, a lower frequency level and a lighter color code are assigned; when the proportion of intersecting areas is in a higher interval, a higher frequency level and a darker color code are assigned. Through this mapping table, the visual attributes of warning signs can be dynamically adjusted according to the size of the proportion of intersecting areas.

[0122] Step S15352: When the ratio of the intersecting areas is in the first interval, a primary warning sign is generated by combining low-frequency flashing and yellow coding.

[0123] When the overlapping area ratio is in the first interval, a primary warning sign is generated using a combination of low-frequency flashing and yellow coding, according to the mapping table settings. Low-frequency flashing means the warning sign flashes at a low frequency, avoiding excessive glare or interference with the user's vision. Yellow coding typically indicates a lower level of warning, conveying information about a relatively minor conflict. Through this combination, the generated primary warning sign can indicate a certain degree of service area overlap without causing excessive anxiety to the user.

[0124] Step S15353: When the ratio of the intersecting areas is in the second interval, a medium-level warning sign is generated by combining medium-frequency flashing and orange coding.

[0125] When the overlap area ratio falls within the second interval, it indicates an increased degree of service area overlap. At this point, a medium-level warning sign is generated using a combination of mid-frequency flashing and orange coding. Mid-frequency flashing occurs at a higher frequency than low-frequency flashing, attracting the user's attention more clearly. Orange coding typically indicates a moderate level of warning, conveying a relatively serious level of conflict. This combination of medium-level warning signs strongly reminds users of the need to pay attention to the overlapping service areas.

[0126] Step S15354: When the ratio of the intersecting areas is in the third interval, a high-level warning sign is generated by combining high-frequency flashing and red coding.

[0127] When the overlap area ratio is in the third interval, it indicates a very serious degree of service area overlap, which may have a significant impact on the operation of the recycling station. In this case, a high-level warning sign is generated using a combination of high-frequency flashing and red coding. The high-frequency flashing warning sign will flash rapidly, greatly attracting the user's attention. Red coding typically indicates a serious warning, conveying the message that immediate action is needed to resolve the conflict. This combination of high-level warning signs strongly reminds users that the service area overlap issue must be addressed as soon as possible.

[0128] Step S15355: Update the visual attributes of the pulse warning sign in real time according to the mapping relationship table, and trigger the corresponding voice prompt information.

[0129] The visual attributes of the pulse warning signs are updated in real time based on a mapping table. As the proportion of intersecting areas changes, the flashing frequency and color depth of the warning signs can be automatically adjusted according to the mapping table. For example, if the proportion of intersecting areas changes from the first interval to the second interval, the warning sign will automatically update from the low-frequency flashing and yellow code of the primary warning sign to the medium-frequency flashing and orange code of the intermediate warning sign.

[0130] Simultaneously, corresponding voice prompts are triggered. Each frequency level and color code combination is assigned a specific voice prompt, which plays synchronously when the visual attributes of the warning sign change. For example, for a basic warning sign, the voice prompt might be "Slight overlap in service areas, please pay attention"; for a medium-level warning sign, the voice prompt might be "Increasing overlap in service areas, please handle promptly"; and for a high-level warning sign, the voice prompt might be "Severe overlap in service areas, immediate action is required." These voice prompts further enhance the reminder effect on users.

[0131] Step S154: Overlay and blend the first marker layer and the second marker layer according to a preset transparency to generate a composite visualization layer.

[0132] The first and second marker layers are overlaid and blended using a preset transparency. The preset transparency is a value set according to actual needs and visual effects, used to control the visibility of the two layers. By adjusting the transparency, the two layers can clearly display their respective content after being overlaid, without obscuring each other or creating visual confusion.

[0133] During the overlay and fusion process, the display order of the two layers is first determined. The first marker layer can be used as the bottom layer, and the second marker layer as the top layer. This ensures that the conflict warning labels and optimization suggestion text are clearly displayed above the recycle bin location label and the service radius ring label. Then, the transparency of the second marker layer is adjusted according to a preset transparency value, allowing it to be overlaid with the first marker layer. The resulting composite visualization layer integrates information such as the recycle bin location, service area, and conflict warning, providing users with a more comprehensive visual display.

[0134] Step S155: In response to the user's interactive operation on the composite visualization layer, dynamically display detailed parameter information in the layout optimization scheme.

[0135] Step S1551: Detect the user's touch trajectory on the composite visualization layer and identify the coordinate range of the target optimization mark.

[0136] Detect user touch trajectories on composite visualization layers. Users can interact with the visualization map interface by touching the screen or using input devices such as a mouse, and the touch trajectories generated by these actions can be monitored in real time.

[0137] Based on the touch trajectory, the system identifies the coordinate range of the target optimization marker. This target optimization marker can be a recycle bin location marker, a service radius ring marker, or a conflict warning marker, etc. The system analyzes the positional relationship between the touch trajectory and each marker to determine the target optimization marker clicked or selected by the user and obtain its coordinate range. For example, if a user clicks a recycle bin location marker, the system can identify the marker's coordinate range on the map to obtain further detailed information.

[0138] Step S1552: Extract the conflict detection index and resource matching score corresponding to the coordinate range from the layout optimization scheme.

[0139] The conflict detection metrics and resource matching scores corresponding to the coordinate ranges are extracted from the layout optimization scheme. The layout optimization scheme contains detailed information for each optimized location, including conflict detection metrics and resource matching scores. Based on the coordinate ranges of the identified target optimization markers, the corresponding records are searched in the layout optimization scheme, and the conflict detection metrics and resource matching scores are extracted. For example, conflict detection metrics may include a first conflict detection metric, a second conflict detection metric, and a third conflict detection metric, etc., while the resource matching score reflects the degree of matching between the location and the reclaimed resources.

[0140] Step S1553: Dynamically load a parameter comparison chart in the sidebar area of ​​the visualization map interface. The parameter comparison chart includes a trend comparison curve of historical data and optimized data.

[0141] The sidebar area of ​​the visualized map interface dynamically loads parameter comparison charts. This sidebar area is a dedicated space on the map interface for displaying detailed information. When a user selects a target optimization marker, a parameter comparison chart is generated based on extracted conflict detection metrics and resource matching scores.

[0142] The parameter comparison chart includes trend curves comparing historical and optimized data. Historical data refers to relevant indicator data for that location or related area before layout optimization, while optimized data is the predicted data for that location in the layout optimization plan. By plotting trend comparison curves, the changing trends of historical and optimized data can be visually displayed, helping users understand the effectiveness of layout optimization. For example, the curve can show changes in indicators such as recycling volume and service area overlap before and after optimization.

[0143] Step S1554: Update the display content and data granularity of the parameter comparison chart in real time according to the comparison dimension selected by the user.

[0144] The parameter comparison chart is updated in real time with its content and data granularity based on the comparison dimensions selected by the user. Users can choose different comparison dimensions, such as time dimension and indicator dimension, through the options on the operation interface. The parameter comparison chart can be regenerated according to the user's selection, and the display content and data granularity can be adjusted.

[0145] For example, if a user chooses to compare changes in recycling volume by quarter, historical and optimized data can be divided by quarter, and the trend comparison curve can be redrawn. The data display precision can also be adjusted to show the changes for each quarter in more detail. By updating the charts in real time, users can conduct in-depth analysis of the layout optimization effects according to their needs.

[0146] Step S1555: When the user triggers the parameter adjustment command, recalculate the layout optimization scheme and refresh the composite visualization layer.

[0147] When a user triggers a parameter adjustment command, it indicates that the user is dissatisfied with the current layout optimization scheme and wishes to make adjustments. Parameter adjustment commands can include the user entering new parameter values ​​on the interface or selecting different optimization strategies.

[0148] After receiving the parameter adjustment instruction, the system will recalculate the layout optimization scheme. This requires inputting the spatial distribution feature set and resource matching feature set into the preset layout optimization model again, and performing dynamic matching analysis based on the new parameters and conditions to generate a new layout optimization scheme.

[0149] Then, based on the new layout optimization scheme, the composite visualization layer is refreshed, which can regenerate the first and second marker layers, update the recycle bin location markers, service radius ring markers, conflict warning markers, and optimization suggestion text, and perform overlay and fusion processing according to the preset transparency. Finally, the updated composite visualization layer is displayed on the visualization map interface, providing users with the latest layout optimization information.

[0150] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0151] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. A method of visual map analysis of a waste paper recycling station layout, characterized in that, The method includes: Obtain a set of historical operation data for multiple historical recycling stations within the target area. The set of historical operation data includes the distribution location information, service coverage information, and recycling volume fluctuation information of each recycling station. Feature extraction is performed on the historical operational data set to generate a spatial distribution feature set and a resource matching feature set for each recycling station; Based on a preset layout optimization model, dynamic matching analysis is performed on the spatial distribution feature set and the resource matching feature set to generate a recycling station layout optimization scheme for the target area. Based on the optimized location coordinates and optimized service radius in the recycling station layout optimization scheme, generate visual map marker data; The map rendering engine is invoked to overlay the visualized map marker data onto the electronic map of the target area, generating a visualized map interface that includes the optimized layout of the recycle bin; The step of extracting features from the historical operational data set to generate a spatial distribution feature set and a resource matching feature set for each recycling station includes: The distance characteristics between each recycling station and its adjacent recycling stations are calculated based on the distribution location information, and coverage overlap characteristics are constructed based on the service coverage information. Based on the information on the fluctuation of the recovery volume, periodic change characteristics are identified, and the difference in recovery volume between adjacent time periods is extracted; Spatial clustering analysis is performed on the spacing features and the coverage overlap features to generate density distribution features that reflect the degree of regional concentration. By performing time-series correlation analysis between the periodic variation characteristics and the recovery volume difference characteristics, load fluctuation characteristics reflecting the degree of dynamic resource matching are generated; The density distribution characteristics and the load fluctuation characteristics are respectively mapped to the spatial distribution characteristic set and the resource matching characteristic set; The method, based on a preset layout optimization model, performs dynamic matching analysis on the spatial distribution feature set and the resource matching feature set to generate a recycling station layout optimization scheme for the target area, including: The density distribution features are input into the first analysis layer of the layout optimization model to generate a set of candidate locations that meet the preset coverage conditions. The load fluctuation characteristics are input into the second analysis layer of the layout optimization model to calculate the resource matching score for each candidate location. The candidate location set is sorted according to the resource matching score, and the optimized location coordinates that meet the score threshold are selected. Based on the historical recovery data of the optimized location coordinates, the boundary range of the optimized service radius is dynamically adjusted; Based on the location relationship between the boundary range and adjacent recycling stations, a layout optimization scheme including conflict detection results is generated; The process of calling the map rendering engine to overlay the visualized map marker data onto the electronic map of the target area generates a visualized map interface containing optimized recycle bin layout markers, including: Extract the geocoding information of the optimized location coordinates and associate it with the boundary coordinate data of the optimized service radius; A first marker layer is generated in the electronic map based on the geocoding information. The first marker layer includes a recycling bin location marker and a service radius ring marker. Identify overlapping areas with existing recycling stations in the boundary coordinate data, and generate a second marking layer. The second marking layer contains conflict warning labels and optimization suggestion text. The first marker layer and the second marker layer are overlaid and blended according to a preset transparency to generate a composite visual layer; In response to user interaction with the composite visualization layer, detailed parameter information of the layout optimization scheme is dynamically displayed.

2. The method of claim 1, wherein, The step of generating a layout optimization scheme including conflict detection results based on the positional relationship between the boundary range and adjacent recycling stations includes: Calculate the difference between the boundary range of the optimized service radius and the preset distance threshold to generate a first conflict detection index; Identify the geographical location data of residential or commercial areas within the boundary range to generate a second conflict detection index; Based on historical recovery data, predict future load peaks within the optimized service radius and generate a third conflict detection index. Input the first conflict detection index, the second conflict detection index and the third conflict detection index into the conflict decision model to generate a conflict level score; When the conflict level score exceeds a preset threshold, the coordinates of the optimized positions that need to be adjusted and the corresponding adjustment priority are marked in the layout optimization scheme.

3. The method of claim 1, wherein, The step of inputting the density distribution features into the first analysis layer of the layout optimization model to generate a set of candidate locations that meet preset coverage conditions includes: Based on the degree of regional concentration in the density distribution characteristics, multiple priority coverage sub-regions are divided; Identify vacant location points that do not meet the service radius requirements in the priority coverage sub-regions with a priority lower than the first set priority; Calculate the overload factor of existing recycling stations in the coverage sub-regions where the priority is higher than the second set priority; By combining the vacant location points with the overload coefficient, a location candidate set containing new candidate points and expansion candidate points is generated; Verify the connectivity characteristics between each candidate point in the location candidate set and the transportation network, and filter out candidate points that do not meet the preset accessibility conditions.

4. The method of claim 3, wherein, The step of verifying the connectivity characteristics of each candidate point in the candidate location set with the transportation network and filtering candidate points that do not meet the preset accessibility conditions includes: Obtain the traffic network topology data of the target area and extract the vehicle traffic density features of the roads where each candidate point is located; Calculate the access distance between each candidate point and the nearest main road, and generate an accessibility score by associating the vehicle traffic density features; Based on historical recycling station vehicle trajectory data, analyze the average transportation time characteristics of each candidate point; The accessibility score and the average transportation time feature are weighted together to generate a comprehensive transportation efficiency index. When the comprehensive transportation efficiency index fails to meet the preset standard, the corresponding candidate point is removed from the candidate location set.

5. The method of claim 1, wherein, The step of identifying overlapping areas with existing recycling stations in the boundary coordinate data and generating a second marker layer includes: Extract the polygon vertex sequence from the boundary coordinate data of the optimized service radius; Calculate the ratio of the intersection area between the polygon vertex sequence and the polygons within the existing recycling bin service area; When the ratio of the intersecting areas exceeds a preset overlap threshold, a pulse warning sign is generated at the center point of the intersecting area; Obtain the historical conflict resolution records of the existing recycling bins and generate optimization suggestion text associated with the pulse warning icon; The flashing frequency and color depth of the pulse warning sign are dynamically adjusted according to the ratio of the intersecting areas. The step of dynamically adjusting the flashing frequency and color depth of the pulse warning sign based on the ratio of the intersecting areas includes: Establish a mapping table between the ratio of intersecting areas and the parameters of warning signs, wherein the parameters of warning signs include frequency level and color code; When the ratio of the intersecting areas is in the first interval, a primary warning sign is generated by combining low-frequency flashing and yellow coding. When the ratio of the intersecting areas is in the second interval, a medium-level warning sign is generated by a combination of medium-frequency flashing and orange coding. When the ratio of the intersecting areas is in the third interval, a high-level warning sign is generated by a combination of high-frequency flashing and red coding. The visual attributes of the pulse warning sign are updated in real time according to the mapping table, and the corresponding voice prompt information is triggered.

6. The method according to claim 1, characterized in that, The step of dynamically displaying detailed parameter information in the layout optimization scheme in response to user interaction with the composite visualization layer includes: Detect the user's touch trajectory on the composite visualization layer and identify the coordinate range of the target optimization mark; Extract the conflict detection index and resource matching score corresponding to the coordinate range from the layout optimization scheme; A parameter comparison chart is dynamically loaded in the sidebar area of ​​the visualization map interface. The parameter comparison chart includes trend comparison curves of historical data and optimized data. The display content and data granularity of the parameter comparison chart are updated in real time according to the comparison dimensions selected by the user. When the user triggers a parameter adjustment command, the layout optimization scheme is recalculated and the composite visualization layer is refreshed.

7. A visualization map analysis system for the layout of waste paper recycling stations, characterized in that, include: The processor, communication interface, memory, and communication bus are provided, wherein the processor, communication interface, and memory communicate with each other via the communication bus. The memory is used to store computer programs; the processor is used to execute the computer programs to implement the steps of the visualization map analysis method for the layout of waste paper recycling stations as described in any one of claims 1-6.

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